Sensitivity of Effluent Variables in Activated Sludge Process
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B Vivekanandan
Abstract
The quality of a treated effluent changes when there is a sudden variation in the influent flow to the wastewater treatment plant during dry, rain, and storm weather conditions. In this study, various influent flow conditions in an activated sludge process are considered that affect the sensitivity of effluent variables such as chemical oxygen demand (COD), biological oxygen demand (BOD), nitrate nitrogen (SNO), ammonical nitrogen (SNH), and total nitrogen (TN) with respect to varying internal recycle flow rate (Qa), sludge recycle flow rate (Qr), sludge wastage flow rate (Qw) and oxygen transfer rate co-efficient of aerobic tanks (KLa(3,4,5)). The analysis has been carried out based on benchmark simulation model no.1 (BSM 1) plant layout which comprises of two models namely activated sludge model no.1 (ASM 1) and simple one dimensional (Simple 1-D) Takacs model. Based on the present analysis, it is observed that the changes in influent flow rate have larger impact on the effluent variables. This variation can be subdued by introducing additional tanks to smoothen the perturbations or using internal recycle rate from the fifth tank in order to maintain the flow around the optimal influent flow rate. The sludge wastage rate has a greater impact on all effluent variables except nitrogenous variables during maximum flow conditions.
References
[1] Guhathakurta P, Sreejith OP, Menon PA. Impact of climate change on extreme rainfall events and flood risk in India. J Earth Syst Sci. 2011;120(3):359–373.10.1007/s12040-011-0082-5Search in Google Scholar
[2] DeGaetano AT. Time-dependent changes in Extreme Precipitation Return-Period amounts in Continental United States. J Appl Meteor Climatol. 2009;48:2086–2099.10.1175/2009JAMC2179.1Search in Google Scholar
[3] Schneiderman ET. Current and future trends in extreme rainfall across New York state 2014. A report from the Environmental Protection Bureau of New York state Attorney General.Search in Google Scholar
[4] Richard OM, Lackey LW, Behrend GH.. The impact of rainfall on flows and loadings at Georgia’s Wastewater Treatment Plants. Proceedings of the 2007 georgia water resources conference, University of Georgia, 2007.Search in Google Scholar
[5] Giokas DL, Vlessidis AG, Angelidis MO, Tsimarakis GJ, Karayannis MI. Systematic analysis of the operational response of activated sludge process to variable wastewater flows. A case study. Clean Techn Environ Policy. 2002;4:183–190.10.1007/s10098-002-0145-zSearch in Google Scholar
[6] Rouleau S, Lessard P, Bellefleur D. Simulation of a transient failure in a wastewater treatment plant: a case study. Water Sci Technol. 1997;36(5):349–355.10.2166/wst.1997.0233Search in Google Scholar
[7] Jin Y, You YX, Ji M. On intensive process of quantity and quality improvement of wastewater treatment plant under rainfall conditions. Desalin Water Treat. 2015;53:330–339.10.1080/19443994.2013.841105Search in Google Scholar
[8] Flores-Tlacuahuac A, Esparza MH, Lopez-Negrete De La Fuente RL. Bifurcation behavior of a large scale waste water treatment plant. Ind Eng Chem Res. 2009;48(5):2605–2615.10.1021/ie8003072Search in Google Scholar
[9] De Araujo ACB, Gallani S, Mulas M, Skogestad S. Sensitivity analysis of optimal operation of an activated sludge process model for economic controlled variable selection. Ind Eng Chem Res. 2013;52(29) 9908−9921.10.1021/ie4006673Search in Google Scholar
[10] Takacs I, Patry GG, Nolasco D. A dynamic model of the clarification-thickening process. Water Res. 2003;25(10):1263–1271.10.1016/0043-1354(91)90066-YSearch in Google Scholar
[11] Henze M, Grady CPL, Gujer W, Marais GVR, Matsuo T. A general model for single-sludge wastewater treatment systems. Water Res. 1987;21:505–515.10.1016/0043-1354(87)90058-3Search in Google Scholar
[12] In: Copp JB, editor(s). The COST Simulation Benchmark-Description and Simulator Manual. Luxembourg: Office for Official Publications of the European Communities, 2002.Search in Google Scholar
[13] Alex J, Benedetti L, Copp J, Gernaey KV, Jeppsson U, Nopens I, et al. Benchmark Simulation Model no. 1 (BSM1). Dept. of Industrial Electrical Engineering and Automation, Lund University, 2008.Search in Google Scholar
[14] Attir U, Denn M. Dynamics and control of activated sludge wasterwater process. Aiche J. 1978;24(4):693–698.10.1002/aic.690240418Search in Google Scholar
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Articles in the same Issue
- Research Articles
- Sensitivity of Effluent Variables in Activated Sludge Process
- Optimization of Pressure-Swing Distillation by Evolutionary Techniques: Separation of Ethanol-Water and Acetonitrile-Water Mixtures
- Phase Split in T-Junction Mini Channel – A Numerical Study
- Simulation and Dynamic Optimization of an Industrial Naphtha Thermal Cracking Furnace Based on Time Variant Feeding Policy
- Mathematical Modeling and Optimization of Syngas Production Process: A Novel Axial Flow Spherical Packed Bed Tri-Reformer
- Estimator Based Inferential Control of an Ideal Quaternary Endothermic Reactive Distillation with Feed-Forward and Recurrent Neural Networks
